Quality evaluation of green tea leaf cultured under artificial light condition using gas chromatography/mass spectrometry.

For an experimental model to elucidate the relationship between light quality during plant culture conditions and plant quality of crops or vegetables, we cultured tea plants (Camellia sinensis) and analyzed their leaves as tea material. First, metabolic profiling of teas from a tea contest in Japan was performed with gas chromatography/mass spectrometry (GC/MS), and then a ranking predictive model was made which predicted tea rankings from their metabolite profile. Additionally, the importance of some compounds (glutamine, glutamic acid, oxalic acid, epigallocatechin, phosphoric acid, and inositol) was elucidated for measurement of the quality of tea leaf. Subsequently, tea plants were cultured in artificial conditions to control these compounds. From the result of prediction by the ranking predictive model, the tea sample supplemented with ultraviolet-A (315-399 nm) showed the highest ranking. The improvement in quality was thought to come from the high amino-acid and decreased epigallocatechin content in tea leaves. The current study shows the use and value of metabolic profiling in the field of high-quality crops and vegetables production that has been conventionally evaluated by human sensory analysis. Metabolic profiling enables us to form hypothesis to understand and develop high quality plant cultured under artificial condition.

High-quality green tea leaf production by artificial cultivation under growth chamber conditions considering amino acids profile.

The current study focused on the tea plant (Camellia sinensis) as a target for artificial cultivation because of the variation in its components in response to light conditions. We analyzed its sensory quality by multi-marker profiling using multicomponent data based on metabolomics to optimize the conditions of light and the environment during cultivation. From the analysis of high-quality tea samples ranked in a tea contest, the ranking predictive model was created by the partial least squares (PLS) regression analysis to examine the correlation between the amino-acid content (X variables) and the ranking in the tea contest (Y variables). The predictive model revealed that glutamine, arginine, and theanine were the predominant amino acids present in high-ranking teas. Based on this result, we established a cover-culture condition (i.e., a low-light intensity condition) during the later stage of the culture process and obtained artificially cultured tea samples, which were predicted to be high-quality teas. The aim of the current study was to optimize the light conditions for the cultivation of tea plants by performing data analysis of their sensory qualities through multi-marker profiling in order to facilitate the development of high-quality teas by plant factories.

Application of Fourier transform near-infrared spectroscopy to optimization of green tea steaming process conditions.

In this study, we constructed prediction models by metabolic fingerprinting of fresh green tea leaves using Fourier transform near-infrared (FT-NIR) spectroscopy and partial least squares (PLS) regression analysis to objectively optimize of the steaming process conditions in green tea manufacture. The steaming process is the most important step for manufacturing high quality green tea products. However, the parameter setting of the steamer is currently determined subjectively by the manufacturer. Therefore, a simple and robust system that can be used to objectively set the steaming process parameters is necessary. We focused on FT-NIR spectroscopy because of its simple operation, quick measurement, and low running costs. After removal of noise in the spectral data by principal component analysis (PCA), PLS regression analysis was performed using spectral information as independent variables, and the steaming parameters set by experienced manufacturers as dependent variables. The prediction models were successfully constructed with satisfactory accuracy. Moreover, the results of the demonstrated experiment suggested that the green tea steaming process parameters could be predicted on a larger manufacturing scale. This technique will contribute to improvement of the quality and productivity of green tea because it can objectively optimize the complicated green tea steaming process and will be suitable for practical use in green tea manufacture.

High-throughput technique for comprehensive analysis of Japanese green tea quality assessment using ultra-performance liquid chromatography with time-of-flight mass spectrometry (UPLC/TOF MS).

Applications of metabolomics techniques along with chemometrics provide an understanding in the relationship between metabolome of green tea and its quality. A coupled of ultra-performance liquid chromatography with time-of-flight mass spectrometry (UPLC/TOF MS) allowed a high-throughput and comprehensive analysis with minimal sample preparation. Using this technique, a wide range of metabolites were investigated. Data analysis was rapid, considering that the fingerprinting technique was performed. A set of green tea samples from 2006 tea contest of the Kansai area was analyzed to prove usefulness of the developed technique. Green tea with different qualities were discriminated through principal component analysis (PCA). Consequently, projection to latent structure by means of partial least-squares (PLS) was performed to create a constructive quality-predictive model by means of metabolic fingerprinting. Beside epigallocatechin, other predominant catechins, including epigallocatechin gallate and epicatechin gallate, detected in green tea were found to be significant biomarkers to the high quality of Japanese green tea (Sencha).

Quality prediction of Japanese green tea using pyrolyzer coupled GC/MS based metabolic fingerprinting.

A couple between pyrolyzer and gas chromatography/mass spectrometry (GC/MS) has allowed a fast, simple, and low-cost approach to evaluate a quality of Japanese green tea without any sample preparation or derivatization techniques. Using our method, errors from sample preparation could be avoided since raw samples were directly extracted through the extreme heat of the pyrolyzer. In addition, undesired reactions from expensive derivatizing agents, which are commonly needed to treat the samples before analyzing with GC/MS, could be omitted. In order to illustrate the efficiency of this technique, a set of green tea samples from the Tea contest in 2005 in the Kansai area were used. Projection to latent structure by means of partial least squares (PLS) along with orthogonal signal correction (OSC) was selected to explain the relation between green tea's metabolite profiling and its quality. The quality of the model was validated by testing and comparing the predictive ability to the respective model.

Prediction of Japanese green tea ranking by fourier transform near-infrared reflectance spectroscopy.

A rapid and easy determination method of green tea's quality was developed by using Fourier transform near-infrared (FT-NIR) reflectance spectroscopy and metabolomics techniques. The method is applied to an online measurement and an online prediction of green tea's quality. FT-NIR was employed to measure green tea metabolites' alteration affected by green tea varieties and manufacturing processes. A set of ranked green tea samples from a Japanese commercial tea contest was analyzed to create a reliable quality-prediction model. As multivariate analyses, principal component analysis (PCA) and partial least-squares projections to latent structures (PLS) were used. It was indicated that the wavenumber region from 5500 to 5200 cm(-1) had high correlation with the quality of the tea. In this study, a reliable quality-prediction model of green tea has been achieved.

Prediction of Japanese green tea ranking by gas chromatography/mass spectrometry-based hydrophilic metabolite fingerprinting.

An innovative technique for green tea's quality determination was developed by means of metabolomics. Gas-chromatography coupled with time-of-flight mass spectrometry and multivariate data analysis was employed to evaluate the quality of green tea. Alteration of green tea varieties and manufacturing processes effects a variation in green tea metabolites, which leads to a classification of the green tea's grade. Therefore, metabolic fingerprinting of green tea samples of different qualities was studied. A set of ranked green tea samples from a Japanese commercial tea contest was analyzed with the aim of creating a reliable quality-prediction model. Several multivariate algorithms were performed. Among those, the partial least-squares projections to latent structures (PLS) analysis with the spectral filtering technique, orthogonal signal correction (OCS), was found to be the most practical approach. In addition, metabolites that play an important role in green tea's grade classification were identified.